Update README.md

README.md

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 ---
 license: apache-2.0
+language:
+- en
+tags:
+- Pytorch
+- mmsegmentation
+- segmentation
+- Flood mapping
+- Sentinel-2
+- Geospatial
+- Foundation model
+metrics:
+- accuracy
+- IoU
 ---
+### Model and Inputs
+The pretrained [Prithvi-100m](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M/blob/main/README.md) parameter model is finetuned to segment the extend of floods on Sentinel-2 images from the [Sen1Floods11 dataset](https://github.com/cloudtostreet/Sen1Floods11). 
+
+The dataset consists of 446 labeled 512x512 chips that span all 14 biomes, 357 ecoregions, and 6 continents of the world across 11 flood events. The benchmark associated to Sen1Floods11 provides results for fully convolutional neural networks trained in various input/labeled data setups, considering Sentinel-1 and Sentinel-2 imagery. 
+
+We extract the following bands for flood mapping:
+ 
+1. Blue
+2. Green
+3. Red
+4. Narrow NIR
+5. SWIR 1
+6. SWIR 2
+
+Labels represent no water (class 0), water/flood (class 1), and no data/clouds (class 2).
+
+The Prithvi-100m model was initially pretrained using a sequence length of 3 timesteps. Based on the characteristics of this benchmark dataset, we focus on single-timestamp segmentation here. This demonstrates that our model does not require multiple timestamps during finetuning.
+
+
+
+### Code
+Code for Finetuning is available through [github](https://github.com/NASA-IMPACT/hls-foundation-os/)
+
+Configuration used for finetuning is available through this [config](https://github.com/NASA-IMPACT/hls-foundation-os/blob/main/fine-tuning-examples/configs/sen1floods11.py).
+
+### Results
+The experiment by running the mmseg stack for 80 epochs using the above config led to the following result:
+
+|     **Classes**    | **IoU**| **Acc**|
+|:------------------:|:------:|:------:|
+|      No water      | 96.90% | 98.11% |
+|     Water/Flood    | 80.46% | 90.54% |
+
+|**aAcc**|**mIoU**|**mAcc**|
+|:------:|:------:|:------:|
+| 97.25% | 88.68% | 94.37% |
+
+
+### Inference
+The github repo includes an inference script that allows to run the flood mapping model for inference on Sentinel-2 images. These input have to be geotiff format, including 6 bands for a single time-step described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in order. There is also a **demo** that leverages the same code **[here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-sen1floods11-demo)**.